原始数据
计算机科学
数据分类
分类器(UML)
数据挖掘
软件可移植性
不确定数据
统计分类
机器学习
人工智能
程序设计语言
作者
Jinchao Huang,Yulin Li,Kaiyue Qi,Fangqi Li
出处
期刊:Lecture notes in electrical engineering
日期:2019-08-14
卷期号:: 345-353
被引量:1
标识
DOI:10.1007/978-981-13-6504-1_43
摘要
Current research on the classification for uncertain data mainly focuses on the structural changes of the classification algorithms. Existing methods have achieved encouraging results; however, they do not take an effective trade-off between accuracy and running time, and they do not have good portability. This paper proposed a new framework to solve the classification problem of uncertain data from data processing point. The proposed algorithm represents the distribution of raw data by a sampling method, which means that the uncertain data are converted into determined data. The proposed framework is suitable for all classifiers, and then, XGBoost is adopted as a specific classifier in this paper. The experimental results show that the proposed method is an effective way of handling the classification problem for uncertain data.
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